Nina Miolane

According to our database, Nina Miolane authored at least 57 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks.

Mathilde Papillon, Guillermo Bernárdez, Claudio Battiloro, Nina Miolane

CoRR, 2024

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree P. Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane

CoRR, 2024

Bounds on the geodesic distances on the Stiefel manifold for a family of Riemannian metrics.

Simon Mataigne, Pierre-Antoine Absil, Nina Miolane

CoRR, 2024

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures.

Sophia Sanborn, Johan Mathe, Mathilde Papillon, Domas Buracas, Hansen Lillemark, Christian Shewmake, Abby Bertics, Xavier Pennec, Nina Miolane

CoRR, 2024

The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks.

Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane

CoRR, 2024

Learning from landmarks, curves, surfaces, and shapes in Geomstats.

Luís F. Pereira, Alice Le Brigant, Adele Myers, Emmanuel Hartman, Amil Khan, Malik Tuerkoen, Trey Dold, Mengyang Gu, Pablo Suárez-Serrato, Nina Miolane

CoRR, 2024

TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning.

Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou

CoRR, 2024

Attending to Topological Spaces: The Cellular Transformer.

Rubén Ballester, Pablo Hernández-García, Mathilde Papillon, Claudio Battiloro, Nina Miolane, Tolga Birdal, Carles Casacuberta, Sergio Escalera, Mustafa Hajij

CoRR, 2024

An efficient algorithm for the Riemannian logarithm on the Stiefel manifold for a family of Riemannian metrics.

Simon Mataigne, Ralf Zimmermann, Nina Miolane

CoRR, 2024

Position Paper: Challenges and Opportunities in Topological Deep Learning.

Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

CoRR, 2024

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.

Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane

CoRR, 2024

Position: Topological Deep Learning is the New Frontier for Relational Learning.

Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On Accuracy and Speed of Geodesic Regression: Do Geometric Priors Improve Learning on Small Datasets?

Adele Myers, Nina Miolane

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Parametric Information Geometry with the Package Geomstats.

Alice Le Brigant, Jules Deschamps, Antoine Collas, Nina Miolane

ACM Trans. Math. Softw., December, 2023

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets.

Wanxin Li, Jules Mirone, Ashok Prasad, Nina Miolane, Carine Legrand, Khanh Dao Duc

Frontiers Bioinform., May, 2023

Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric.

Saiteja Utpala, Praneeth Vepakomma, Nina Miolane

Trans. Mach. Learn. Res., 2023

Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats.

Nicolas Guigui, Nina Miolane, Xavier Pennec

Found. Trends Mach. Learn., 2023

Identifying Interpretable Visual Features in Artificial and Biological Neural Systems.

David A. Klindt, Sophia Sanborn, Francisco Acosta, Frédéric Poitevin, Nina Miolane

CoRR, 2023

Reconstructing Heterogeneous Cryo-EM Molecular Structures by Decomposing Them into Polymer Chains.

Bongjin Koo, Julien N. P. Martel, Ariana Peck, Axel Levy, Frédéric Poitevin, Nina Miolane

CoRR, 2023

Architectures of Topological Deep Learning: A Survey on Topological Neural Networks.

Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane

CoRR, 2023

ICML 2023 Topological Deep Learning Challenge: Design and Results.

Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou, Aldo Guzmán-Sáenz, Bastian Rieck, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane

Proceedings of the Topological, 2023

Preface.

Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn

Proceedings of the Topological, 2023

A General Framework for Robust G-Invariance in G-Equivariant Networks.

Sophia Sanborn, Nina Miolane

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation.

Adele Myers, Caitlin M. Taylor, Emily G. Jacobs, Nina Miolane

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Group Equivariant Sparse Coding.

Christian Shewmake, Nina Miolane, Bruno A. Olshausen

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

Quantifying Extrinsic Curvature in Neural Manifolds.

Francisco Acosta, Sophia Sanborn, Khanh Dao Duc, Manu S. Madhav, Nina Miolane

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Testing geometric representation hypotheses from simulated place cell recordings.

Thibault Niederhauser, Adam Lester, Nina Miolane, Khanh Dao Duc, Manu S. Madhav

CoRR, 2022

Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves.

Adele Myers, Nina Miolane

CoRR, 2022

Heterogeneous reconstruction of deformable atomic models in Cryo-EM.

Youssef S. G. Nashed, Ariana Peck, Julien N. P. Martel, Axel Levy, Bongjin Koo, Gordon Wetzstein, Nina Miolane, Daniel Ratner, Frédéric Poitevin

CoRR, 2022

Intentional Choreography with Semi-Supervised Recurrent VAEs.

Mathilde Papillon, Mariel Pettee, Nina Miolane

CoRR, 2022

PirouNet: Creating Intentional Dance with Semi-Supervised Conditional Recurrent Variational Autoencoders.

Mathilde Papillon, Mariel Pettee, Nina Miolane

CoRR, 2022

Defining an action of SO(d)-rotations on images generated by projections of d-dimensional objects: Applications to pose inference with Geometric VAEs.

Nicolas Legendre, Khanh Dao Duc, Nina Miolane

CoRR, 2022

ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results.

Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Umberto Lupo, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane

CoRR, 2022

Higher-Order Attention Networks.

Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy

CoRR, 2022

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy.

Claire Donnat, Axel Levy, Frédéric Poitevin, Nina Miolane

CoRR, 2022

ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results.

Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane

Proceedings of the Topological, 2022

Preface.

Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf

Proceedings of the Topological, 2022

Preface.

Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane

Proceedings of the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2022

CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.

Axel Levy, Frédéric Poitevin, Julien N. P. Martel, Youssef S. G. Nashed, Ariana Peck, Nina Miolane, Daniel Ratner, Mike Dunne, Gordon Wetzstein

Proceedings of the Computer Vision - ECCV 2022, 2022

PirouNet: Creating Dance Through Artist-Centric Deep Learning.

Mathilde Papillon, Mariel Pettee, Nina Miolane

Proceedings of the ArtsIT, Interactivity and Game Creation, 2022

2021

Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks.

Faria Huq, Adrish Dey, Sahra Yusuf, Dena Bazazian, Tolga Birdal, Nina Miolane

CoRR, 2021

ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results.

Nina Miolane, Matteo Caorsi, Umberto Lupo, Marius Guerard, Nicolas Guigui, Johan Mathe, Yann Cabanes, Wojciech Reise, Thomas Davies, António Leitão, Somesh Mohapatra, Saiteja Utpala, Shailja Shailja, Gabriele Corso, Guoxi Liu, Federico Iuricich, Andrei Manolache, Mihaela Nistor, Matei Bejan, Armand Mihai Nicolicioiu, Bogdan-Alexandru Luchian, Mihai-Sorin Stupariu, Florent Michel, Khanh Dao Duc, Bilal Abdulrahman, Maxim Beketov, Elodie Maignant, Zhiyuan Liu, Marek Cerný, Martin Bauw, Santiago Velasco-Forero, Jesús Angulo, Yanan Long

CoRR, 2021

Using a Riemannian Elastic Metric for Statistical Analysis of Tumor Cell Shape Heterogeneity.

Wanxin Li, Ashok Prasad, Nina Miolane, Khanh Dao Duc

Proceedings of the Geometric Science of Information - 6th International Conference, 2021

2020

Geomstats: A Python Package for Riemannian Geometry in Machine Learning.

Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec

CoRR, 2020

Introduction to Geometric Learning in Python with Geomstats.

Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Daniel Brooks, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec

Proceedings of the 19th Python in Science Conference 2020 (SciPy 2020), Virtual Conference, July 6, 2020

A {B.

Claire Donnat, Nina Miolane, Freddy Bunbury, Jack Kreindler

Proceedings of the Machine Learning for Health Workshop, 2020

Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks.

Nina Miolane, Frédéric Poitevin, Yee-Ting Li, Susan P. Holmes

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders.

Nina Miolane, Susan P. Holmes

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction.

Johan Mathe, Nina Miolane, Nicolas Sébastien, Jeremie Lequeux

CoRR, 2019

2018

Topologically Constrained Template Estimation via Morse-Smale Complexes Controls Its Statistical Consistency.

Nina Miolane, Susan P. Holmes, Xavier Pennec

SIAM J. Appl. Algebra Geom., 2018

geomstats: a Python Package for Riemannian Geometry in Machine Learning.

Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec

CoRR, 2018

Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry.

Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven G. McDonagh, Joseph V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

2017

Template Shape Estimation: Correcting an Asymptotic Bias.

Nina Miolane, Susan P. Holmes, Xavier Pennec

SIAM J. Imaging Sci., 2017

2016

Geometric Statistics for Computational Anatomy. (Les statistiques géométriques pour l'anatomie numérique).

Nina Miolane

PhD thesis, 2016

2015

Computing Bi-Invariant Pseudo-Metrics on Lie Groups for Consistent Statistics.

Nina Miolane, Xavier Pennec

Entropy, 2015

A Survey of Mathematical Structures for Extending 2D Neurogeometry to 3D Image Processing.

Nina Miolane, Xavier Pennec

Proceedings of the Medical Computer Vision: Algorithms for Big Data, 2015

Biased Estimators on Quotient Spaces.

Nina Miolane, Xavier Pennec

Proceedings of the Geometric Science of Information - Second International Conference, 2015